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Figure 5. Comparison between modelled and observed water level (storm tide) for the period of 10 December 2014 to 2 January 2015 at the tide gauge station “Quai-Nord” of the Port of Abidjan.

Figure 5 Comparison between modelled and observed water level (storm tide) for the period of 10 December 2014 to 2 January 2015 at the tide gauge station “Quai-Nord” of the Port of Abidjan.

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Figure 1. Map of the Study Area: (A) Cote d’Tvoire context; (B) the entire city of Port-Bouét; (C) the sector of Port-Bouét Bay with the coastline subdivided according to the three bordering neighbor- hoods: Vridi (in blue), Petit-Bassam (in green) and Sogefiha (in yellow). Background outlines in (B,C) are from Esri Topographic and standard OpenStreetMap standard, respectively. sector of Port-Bouét Bay with the coastline subdivided according to the three bordering neighbor-
Figure 1. Map of the Study Area: (A) Cote d’Tvoire context; (B) the entire city of Port-Bouét; (C) the sector of Port-Bouét Bay with the coastline subdivided according to the three bordering neighbor- hoods: Vridi (in blue), Petit-Bassam (in green) and Sogefiha (in yellow). Background outlines in (B,C) are from Esri Topographic and standard OpenStreetMap standard, respectively. sector of Port-Bouét Bay with the coastline subdivided according to the three bordering neighbor-
Solving these equations is equivalent to determining each element that composes them. However, the process is not straightforward because extreme sea level (ESL) events are rare and can only be accurately understood by observing sea level over a long period of time [33]. In other words, the calculation of ESL requires historical sea level data that is both long and of high quality. For accurate ESL calculations, historical sea level data should ideally be measured at a reference point (datum), sampled at hourly intervals, and free of any data gaps. Meeting these criteria is generally challenging, particularly for developing coastal countries that may lack the necessary resources and equipment. To overcome the lack of quality data, techniques have been developed to calculate extremes from shorter records [34]. Other techniques involve reconstructing historical sea level time series to increase the necessary data length and/or to fill any gaps. The latter techniques have the advantage of enabling efficient extreme analysis and thus provide accurate estimates. In this study, we opted to reconstruct sea level records over the past 40 years for both storm tide and wave run-up using various modeling methods. Figure 2 provides an overview of the methodological approach. Below, we provide detailed explanations of the modeling process, statistical methods, and data used. Figure 2. Methodological Approach. “Extr.” stands for Extreme; “ST” for Storm Tide, and th acronym “ESLs” denotes Extreme Sea Levels.
Solving these equations is equivalent to determining each element that composes them. However, the process is not straightforward because extreme sea level (ESL) events are rare and can only be accurately understood by observing sea level over a long period of time [33]. In other words, the calculation of ESL requires historical sea level data that is both long and of high quality. For accurate ESL calculations, historical sea level data should ideally be measured at a reference point (datum), sampled at hourly intervals, and free of any data gaps. Meeting these criteria is generally challenging, particularly for developing coastal countries that may lack the necessary resources and equipment. To overcome the lack of quality data, techniques have been developed to calculate extremes from shorter records [34]. Other techniques involve reconstructing historical sea level time series to increase the necessary data length and/or to fill any gaps. The latter techniques have the advantage of enabling efficient extreme analysis and thus provide accurate estimates. In this study, we opted to reconstruct sea level records over the past 40 years for both storm tide and wave run-up using various modeling methods. Figure 2 provides an overview of the methodological approach. Below, we provide detailed explanations of the modeling process, statistical methods, and data used. Figure 2. Methodological Approach. “Extr.” stands for Extreme; “ST” for Storm Tide, and th acronym “ESLs” denotes Extreme Sea Levels.
Figure 3. Sea level rise projections for the SSP1-2.6 (in blue), SSP2-4.5 (in yellow), and SSP5-8.5 (in red) scenarios off the coast of Port-Bouét, based on data from the NASA Sea Level Projection Tool. The solid lines indicate the median range, while the shaded areas represent the 17th to 83rd percentile ranges (the likely ranges).
Figure 3. Sea level rise projections for the SSP1-2.6 (in blue), SSP2-4.5 (in yellow), and SSP5-8.5 (in red) scenarios off the coast of Port-Bouét, based on data from the NASA Sea Level Projection Tool. The solid lines indicate the median range, while the shaded areas represent the 17th to 83rd percentile ranges (the likely ranges).
Hydrodynamic modelling of storm tide allows spatial extrapolation of water level from the tide-gauge locations (generally sheltered areas) to other surrounding areas, e.g., the open coasts. It also helps in filling small to large gaps in the water level records. In this work, storm tide simulations were undertaken using the D-Flow Flexible Mesh software developed by Deltares. This tool solves the depth-averaged shallow water equations. Detailed information on the model formulation can be found in [50]. The model domain includes the marine environment and the entire Ebrié lagoon system, both being connected through the Vridi canal (see Figure 4). This was necessary to account for the interactions between the sea and the lagoon system, which could somehow influence water levels in the area of interest. The computational grid was updated from the one of Briere et al. [51]. It is an unstructured grid made up of curvilinear and triangular meshes. The mesh resolution gradually increases as we move towards the study area and the port domain where the tidal station is located. Orthogonality and smoothness are two important grid properties for model accuracy. They respectively denote perpendicularity between flow links and net links and the ratio of neighboring grid cell dimensions. The grid is in good agreement with the orthogonality and smoothness thresholds defined in the modeling guidelines [52] and therefore satisfies the grid quality requirements. The depth schematization of the computational grids has been carried out by interpolating the different sets of bathymetric data described earlier. Figure 4. Model domain: (A) shows the computational grid built to set up the hydrodynamic model, and (B) displays the Bathymetry of Port-Bouét Bay and its surrounding areas. Background outlines in both (A,B) are from Esri Imagery.
Hydrodynamic modelling of storm tide allows spatial extrapolation of water level from the tide-gauge locations (generally sheltered areas) to other surrounding areas, e.g., the open coasts. It also helps in filling small to large gaps in the water level records. In this work, storm tide simulations were undertaken using the D-Flow Flexible Mesh software developed by Deltares. This tool solves the depth-averaged shallow water equations. Detailed information on the model formulation can be found in [50]. The model domain includes the marine environment and the entire Ebrié lagoon system, both being connected through the Vridi canal (see Figure 4). This was necessary to account for the interactions between the sea and the lagoon system, which could somehow influence water levels in the area of interest. The computational grid was updated from the one of Briere et al. [51]. It is an unstructured grid made up of curvilinear and triangular meshes. The mesh resolution gradually increases as we move towards the study area and the port domain where the tidal station is located. Orthogonality and smoothness are two important grid properties for model accuracy. They respectively denote perpendicularity between flow links and net links and the ratio of neighboring grid cell dimensions. The grid is in good agreement with the orthogonality and smoothness thresholds defined in the modeling guidelines [52] and therefore satisfies the grid quality requirements. The depth schematization of the computational grids has been carried out by interpolating the different sets of bathymetric data described earlier. Figure 4. Model domain: (A) shows the computational grid built to set up the hydrodynamic model, and (B) displays the Bathymetry of Port-Bouét Bay and its surrounding areas. Background outlines in both (A,B) are from Esri Imagery.
Table 1. Representative beach slopes ((¢) calculated from historical beach profiles. The beach slope parameter ; in this study is defined, according to Ruggiero et al. [61] and Stockdon et al. [58], as the linear average slope over the beach face, which is the region of observed run-up +20, where o is the standard deviation of the existing water-level record. Bs was derived from historical beach profiles surveyed at several points across the study area. Slopes were first calculated for individual profiles, then averaged for each survey point. The representative beach slopes of each neighborhood (see Table 1) were obtained by averaging the slopes of surveyed points along each neighborhood.
Table 1. Representative beach slopes ((¢) calculated from historical beach profiles. The beach slope parameter ; in this study is defined, according to Ruggiero et al. [61] and Stockdon et al. [58], as the linear average slope over the beach face, which is the region of observed run-up +20, where o is the standard deviation of the existing water-level record. Bs was derived from historical beach profiles surveyed at several points across the study area. Slopes were first calculated for individual profiles, then averaged for each survey point. The representative beach slopes of each neighborhood (see Table 1) were obtained by averaging the slopes of surveyed points along each neighborhood.
Equation (4) was used to reconstruct historical wave run-up time series of the same length as the extracted offshore wave climate, i.e., 40 years of hourly wave run-up data However, the validation of these estimations is crucial before their use in the various analyses. Checking the validity of run-up estimates in a wave data scare location like in this study area is not easy work. Here, the calculated wave run-up was simply com. pared to the observed run-up elevation derived from satellite imagery [62]. The run-up boundaries can be seen on satellite images as the demarcation lines between the wet and dry beaches (Figure 6). Wet beaches generally appear darker in color than dry beaches on satellite images.
Equation (4) was used to reconstruct historical wave run-up time series of the same length as the extracted offshore wave climate, i.e., 40 years of hourly wave run-up data However, the validation of these estimations is crucial before their use in the various analyses. Checking the validity of run-up estimates in a wave data scare location like in this study area is not easy work. Here, the calculated wave run-up was simply com. pared to the observed run-up elevation derived from satellite imagery [62]. The run-up boundaries can be seen on satellite images as the demarcation lines between the wet and dry beaches (Figure 6). Wet beaches generally appear darker in color than dry beaches on satellite images.
Table 2. Statistical error parameters were obtained from a comparison between observed anc calculated sea levels. 2.4.3. Extreme Value Analysis
Table 2. Statistical error parameters were obtained from a comparison between observed anc calculated sea levels. 2.4.3. Extreme Value Analysis
Figure 7. Return value plots of the GPD-POT model fitted to storm tide data (a) and wave run-up (b) and their associated 95% confidence intervals at Vridi (A), Petit-Bassam (B) and Sogefiha (C). The blue asterisks represent the storm tide or wave run-up levels over the calculated thresholds (extreme values); the plain blue lines are the best-fit distribution to the extreme values; the dashed blue lines show the 95% confidence limits of the fit; and the red horizontal lines are the asymptote lines to the best-fit curves. Applying the GPD-POT method to the modelled time series of storm tide and wave run-up helps to produce extreme return levels for different return periods. Figure 7 shows the return value plots for the order of 1- to 100-year return period for each of the selected neighborhoods.
Figure 7. Return value plots of the GPD-POT model fitted to storm tide data (a) and wave run-up (b) and their associated 95% confidence intervals at Vridi (A), Petit-Bassam (B) and Sogefiha (C). The blue asterisks represent the storm tide or wave run-up levels over the calculated thresholds (extreme values); the plain blue lines are the best-fit distribution to the extreme values; the dashed blue lines show the 95% confidence limits of the fit; and the red horizontal lines are the asymptote lines to the best-fit curves. Applying the GPD-POT method to the modelled time series of storm tide and wave run-up helps to produce extreme return levels for different return periods. Figure 7 shows the return value plots for the order of 1- to 100-year return period for each of the selected neighborhoods.
Figure 8. Seasonal distribution of occurrences of extreme wave run-up higher than 1-year, 10-year and 25-year return levels over the period 1981-2020. From Figure 7, it is clear that the wave run-up contributes more to the total ESL as compared to the storm tide levels. For example, at Vridi, the 100-year return level of wave run-up is estimated as 3.69 m against 0.69 m for the storm tide (Figure 7a,b). Additionally, it can be seen that over the past 40 years, storm tide was quite stable in terms of temporal variability, whereas extreme wave run-up underwent both seasonal and inter-annual variations. The maximum run-u event of 3 and 4 September 2016. At this particular event, the calc 4.19 m and 3.72 m, respectively, for Vridi, Peti of extreme wave run-up > 1-year return leve p heights occurred -Bassam and Soge during the meteorological ulated run-up gave 3.61 m, fiha. About 37 occurrences were identified within Port-Bouét Bay from 1981 to 2020. Among these, six were > 10-year, and three were > 25-year return levels. The seasonal distribution of these extreme wave events (Figure 8) s > 1-year return levels occur from April to November and are month of August. During the assessment, no extreme wave run-up > 1-year return level was observed in January and December. hows that extreme run-up most frequent during the
Figure 8. Seasonal distribution of occurrences of extreme wave run-up higher than 1-year, 10-year and 25-year return levels over the period 1981-2020. From Figure 7, it is clear that the wave run-up contributes more to the total ESL as compared to the storm tide levels. For example, at Vridi, the 100-year return level of wave run-up is estimated as 3.69 m against 0.69 m for the storm tide (Figure 7a,b). Additionally, it can be seen that over the past 40 years, storm tide was quite stable in terms of temporal variability, whereas extreme wave run-up underwent both seasonal and inter-annual variations. The maximum run-u event of 3 and 4 September 2016. At this particular event, the calc 4.19 m and 3.72 m, respectively, for Vridi, Peti of extreme wave run-up > 1-year return leve p heights occurred -Bassam and Soge during the meteorological ulated run-up gave 3.61 m, fiha. About 37 occurrences were identified within Port-Bouét Bay from 1981 to 2020. Among these, six were > 10-year, and three were > 25-year return levels. The seasonal distribution of these extreme wave events (Figure 8) s > 1-year return levels occur from April to November and are month of August. During the assessment, no extreme wave run-up > 1-year return level was observed in January and December. hows that extreme run-up most frequent during the
In terms of the inter-annual distribution of extreme wave run-up occurrences, it has been that there has been an intensification of extreme wave conditions in Port-Bouét Bay. Both the magnitude and the number of wave run-up occurrences have increased in these recent decades. As an illustration of this, Figure 9 displays the clustering of peaks exceeding the 1-year and 10-year return levels in the neighborhood of Sogefiha. The 1-year and 10-year return levels correspond to 2.81 m and 3.35 m, respectively, as shown in Figure 7b. It can be observed in Figure 9 that approximately 75% of the extreme run-up events > 1-year return level and 83% of those > 10-year return levels occurred during the past two decades.
In terms of the inter-annual distribution of extreme wave run-up occurrences, it has been that there has been an intensification of extreme wave conditions in Port-Bouét Bay. Both the magnitude and the number of wave run-up occurrences have increased in these recent decades. As an illustration of this, Figure 9 displays the clustering of peaks exceeding the 1-year and 10-year return levels in the neighborhood of Sogefiha. The 1-year and 10-year return levels correspond to 2.81 m and 3.35 m, respectively, as shown in Figure 7b. It can be observed in Figure 9 that approximately 75% of the extreme run-up events > 1-year return level and 83% of those > 10-year return levels occurred during the past two decades.
Table 3. Present-day ESLs deduced from the Joint Probability Method (in meters). 3.3. Future ESLs
Table 3. Present-day ESLs deduced from the Joint Probability Method (in meters). 3.3. Future ESLs
Figure 10. Future of the present-day 100-year return level under different SLR Scenarios at Vridi (A), Petit-Bassam (B) and Sogefiha (C). Red horizontal lines represent the present-day 100-year return level (2020). The vertical error bars represent the 17th—-83rd percentiles (likely range) for the years 2030, 2050 and 2100. The future ESLs were determined by combining the projected regional sea-level rise (SLR) with the present-day ESLs as stated in the approach overview. However, due to the use of this regional SLR projection, differentiation of SLR at the local scale for Vridi, Petit-Bassam and Sogefiha was not possible. Figure 10 shows calculation results for future 100-year return levels under different climate change scenarios.
Figure 10. Future of the present-day 100-year return level under different SLR Scenarios at Vridi (A), Petit-Bassam (B) and Sogefiha (C). Red horizontal lines represent the present-day 100-year return level (2020). The vertical error bars represent the 17th—-83rd percentiles (likely range) for the years 2030, 2050 and 2100. The future ESLs were determined by combining the projected regional sea-level rise (SLR) with the present-day ESLs as stated in the approach overview. However, due to the use of this regional SLR projection, differentiation of SLR at the local scale for Vridi, Petit-Bassam and Sogefiha was not possible. Figure 10 shows calculation results for future 100-year return levels under different climate change scenarios.
Figure 11. Evolution of the return period for the current 100-year return level over time under the different climate change scenarios for each neighborhood in Port-Bouét Bay. While it is interesting to demonstrate the impact of SLR on future ESL elevations, relying solely on such data for coastal management and planning without considering the potential changes in ESL frequencies could be limiting. Therefore, we analyzed changes over time and under SSPs in the current 100-year return period, as illustrated in Figure 11. This figure shows that in Port-Bouét Bay, the current 100-year return period will decrease in all neighborhoods. Over the next decade, today’s 100-year ESLs will have return periods ranging from 52 to 63 years. This could decrease to between 24 and 37 years, i.e., more than doubling the current frequency by mid-century. By 2100, the current once-a-century ESLs in Port-Bouét Bay will occur nearly every 2 years under SSP5-8.5, and between 8 to 2 years under SSP1-2.6 and 5 to 7 years under SSP2-4.5. The neighborhood of Vridi is ikely to be the most vulnerable in terms of future ESL frequency, followed by Sogefiha and Petit-Bassam in that order. This analysis allows understanding that even minor, short-term changes in SLR could significantly affect ESL frequencies. By the end of the century, ESLs hat are currently infrequent in Port-Bouét Bay will become relatively common, regardless of the climate change scenario.
Figure 11. Evolution of the return period for the current 100-year return level over time under the different climate change scenarios for each neighborhood in Port-Bouét Bay. While it is interesting to demonstrate the impact of SLR on future ESL elevations, relying solely on such data for coastal management and planning without considering the potential changes in ESL frequencies could be limiting. Therefore, we analyzed changes over time and under SSPs in the current 100-year return period, as illustrated in Figure 11. This figure shows that in Port-Bouét Bay, the current 100-year return period will decrease in all neighborhoods. Over the next decade, today’s 100-year ESLs will have return periods ranging from 52 to 63 years. This could decrease to between 24 and 37 years, i.e., more than doubling the current frequency by mid-century. By 2100, the current once-a-century ESLs in Port-Bouét Bay will occur nearly every 2 years under SSP5-8.5, and between 8 to 2 years under SSP1-2.6 and 5 to 7 years under SSP2-4.5. The neighborhood of Vridi is ikely to be the most vulnerable in terms of future ESL frequency, followed by Sogefiha and Petit-Bassam in that order. This analysis allows understanding that even minor, short-term changes in SLR could significantly affect ESL frequencies. By the end of the century, ESLs hat are currently infrequent in Port-Bouét Bay will become relatively common, regardless of the climate change scenario.
Related topics:
Environmental ScienceClimate ChangeStorm SurgesLong Wave Run-upGeneralized Pareto DistributionLocal scale studyjoint probability method
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